Marine plastic pollution has emerged as a significant ecological and biological issue impacting global marine ecosystems. To develop real-time cleaning systems for marine plastic litter, we implemented a three-dimensional marine plastic litter real-time detection (3D-MPLRD) system that incorporates deep learning techniques. Specifically, image quality assessment and enhancement are applied to mitigate the adverse effects of harsh underwater conditions on image quality, thereby enhancing the training efficacy of models beyond that achieved by conventional approaches. To facilitate the deployment of the 3D-MPLRD system on embedded devices, the YOLOv5 model is compressed and quantified, capitalizing on its robustness to precision loss and model simplification. Practical experimental results demonstrated that the 3D-MPLRD system outperformed the model trained on original datasets in terms of precision, recall, F1-score, and mean average precision. This innovative 3D-MPLRD system serves as a foundational reference for marine environmental protection based on intelligence systems, as opposed to traditional manual methods.